CN115879357A - Self-adaptive bias proportion guidance method based on neural network - Google Patents
Self-adaptive bias proportion guidance method based on neural network Download PDFInfo
- Publication number
- CN115879357A CN115879357A CN202111137643.7A CN202111137643A CN115879357A CN 115879357 A CN115879357 A CN 115879357A CN 202111137643 A CN202111137643 A CN 202111137643A CN 115879357 A CN115879357 A CN 115879357A
- Authority
- CN
- China
- Prior art keywords
- neural network
- guidance
- initial
- angle
- adaptive bias
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000004088 simulation Methods 0.000 claims description 30
- 238000012549 training Methods 0.000 claims description 19
- 230000003044 adaptive effect Effects 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 6
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 abstract description 4
- 230000004438 eyesight Effects 0.000 abstract 1
- 230000003068 static effect Effects 0.000 abstract 1
- 230000006870 function Effects 0.000 description 6
- 238000013507 mapping Methods 0.000 description 4
- 230000000052 comparative effect Effects 0.000 description 3
- 238000009795 derivation Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000010200 validation analysis Methods 0.000 description 3
- 230000004913 activation Effects 0.000 description 2
- 101001121408 Homo sapiens L-amino-acid oxidase Proteins 0.000 description 1
- 101000827703 Homo sapiens Polyphosphoinositide phosphatase Proteins 0.000 description 1
- 102100026388 L-amino-acid oxidase Human genes 0.000 description 1
- 102100023591 Polyphosphoinositide phosphatase Human genes 0.000 description 1
- 101100012902 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) FIG2 gene Proteins 0.000 description 1
- 101100233916 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) KAR5 gene Proteins 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Feedback Control In General (AREA)
Abstract
Description
技术领域Technical Field
本发明涉及一种基于神经网络的自适应偏置比例导引方法,属飞行器控制技术领域。The invention relates to an adaptive bias proportional guidance method based on a neural network, belonging to the technical field of aircraft control.
背景技术Background Art
制导律的选取对终端交会角的精度起关键作用。The selection of guidance law plays a key role in the accuracy of the terminal intersection angle.
目前,偏置比例导引法被广泛应用,其是在传统的比例导引律基础上,附加常值偏置项,该方法具有所需信息量较少、结构简单等优点,尤其是无需估算飞行剩余时间,因而在工程应用上拥有明显的优势。At present, the biased proportional navigation method is widely used. It adds a constant bias term to the traditional proportional navigation law. This method has the advantages of less required information and simple structure. In particular, there is no need to estimate the remaining flight time. Therefore, it has obvious advantages in engineering applications.
然而,偏置项的精确程度直接影响制导精度,传统公式由于推导时应用小角假设及估计飞行总时间时所应用的公式等均存在误差,影响终端交会角的精度。However, the accuracy of the bias term directly affects the guidance accuracy. The traditional formula has errors due to the small angle assumption used in the derivation and the formula used to estimate the total flight time, which affects the accuracy of the terminal intersection angle.
此外,传统的公式推导方法,需要专业技术人员根据实际初始条件临时计算推导偏置项,使用限制较多,计算推导工作量较大。In addition, the traditional formula derivation method requires professional technicians to temporarily calculate and derive the bias terms based on the actual initial conditions. It has many usage restrictions and a large workload for calculation and derivation.
由于上述原因,有必要提出了一种能够在线快速获得的精确度较高的偏置比例导引法。Due to the above reasons, it is necessary to propose a bias proportional guidance method with high accuracy that can be obtained quickly online.
发明内容Summary of the invention
为了克服上述问题,本发明人进行了深入研究,设计出一种基于神经网络的自适应偏置比例导引方法,针对静止固定目标,利用神经网络获取偏置比例导引中的常数项。In order to overcome the above problems, the inventors conducted in-depth research and designed an adaptive bias proportional guidance method based on a neural network. For a stationary fixed target, a neural network is used to obtain the constant term in the bias proportional guidance.
进一步地,所述神经网络为BP神经网络。Furthermore, the neural network is a BP neural network.
在一个优选的实施方式中,所述神经网络的输入为飞行器发射时的弹目距离r、初始弹道倾角θ0,初始弹目视线角q0和期望的终端交会角θf,所述BP神经网络的输出为常数项b。In a preferred embodiment, the input of the neural network is the missile-target distance r, the initial trajectory inclination angle θ 0 , the initial missile-target sight angle q 0 and the expected terminal intersection angle θ f when the aircraft is launched, and the output of the BP neural network is the constant term b.
在一个优选的实施方式中,根据下式获得偏置比例导引的导引律:In a preferred embodiment, the guidance law of the biased proportional guidance is obtained according to the following formula:
其中,θ为弹道倾角,q为弹目视线角,N为导引系数。Among them, θ is the ballistic inclination angle, q is the sight angle between the projectile and the target, and N is the guidance coefficient.
在一个优选的实施方式中,所述BP神经网络的隐藏层的个数为5个。In a preferred embodiment, the number of hidden layers of the BP neural network is 5.
在一个优选的实施方式中,在使用神经网络前需要对其进行训练,采用弹道仿真获得训练样本。In a preferred embodiment, the neural network needs to be trained before use, and trajectory simulation is used to obtain training samples.
在一个优选的实施方式中,所述弹道仿真为非线性打击模型仿真,所述非线性打击模型可以表示为:In a preferred embodiment, the trajectory simulation is a nonlinear strike model simulation, and the nonlinear strike model can be expressed as:
η=θ-qη=θ-q
其中,η为飞行器速度与弹目视线的夹角,θ为弹道倾角,q为弹目视线角,r为弹目距离,v为飞行器速度,aM为飞行器的过载指令。Among them, η is the angle between the aircraft speed and the missile-target line of sight, θ is the trajectory inclination angle, q is the missile-target line of sight angle, r is the missile-target distance, v is the aircraft speed, and aM is the overload command of the aircraft.
在一个优选的实施方式中,在建立训练样本时,多次修改弹道仿真的设定参数,获得不同设定参数下的终端交会角,所述弹道仿真的设定参数包括初始弹目距离、导引系数、初始弹道倾角、初始弹目视线角。In a preferred embodiment, when establishing training samples, the setting parameters of the trajectory simulation are modified multiple times to obtain the terminal intersection angle under different setting parameters. The setting parameters of the trajectory simulation include the initial projectile-target distance, the guidance coefficient, the initial trajectory inclination angle, and the initial projectile-target line of sight angle.
在一个优选的实施方式中,初始弹目距离为5000米-10000米,导引系数2-4,初始弹道倾角10-20°。In a preferred embodiment, the initial projectile-target distance is 5000 meters to 10000 meters, the guidance coefficient is 2-4, and the initial ballistic inclination angle is 10-20 degrees.
在一个优选的实施方式中,在训练神经网络的过程中,采用Adam学习率对神经网络的参数进行更新。In a preferred embodiment, during the training of the neural network, the parameters of the neural network are updated using the Adam learning rate.
本发明所具有的有益效果包括:The beneficial effects of the present invention include:
(1)采用BP神经网络实现了该映射的高精度拟合逼近,制导精度高;(1) The BP neural network is used to achieve high-precision fitting approximation of the mapping, with high guidance accuracy;
(2)可在不同的初始条件和约束下,在线完成偏置比例导引的参数求解,使用灵活;(2) The bias proportional guidance parameter solution can be completed online under different initial conditions and constraints, which is flexible to use;
(3)减少了传统公式求解时所需的计算量,在工程应用中减少了计算成本。(3) It reduces the amount of calculation required to solve traditional formulas and reduces the computational cost in engineering applications.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1示出根据本发明一种优选实施方式的一种基于神经网络的自适应偏置比例导引方法流程示意图;FIG1 shows a schematic flow chart of an adaptive bias proportional guidance method based on a neural network according to a preferred embodiment of the present invention;
图2示出实施例1中导引律的仿真弹道轨迹;FIG2 shows the simulated ballistic trajectory of the guidance law in Example 1;
图3示出实施例1中导引律的仿真弹道倾角;FIG3 shows the simulated trajectory inclination angle of the guidance law in Example 1;
图4示出根据本发明实施例2和对比例1中仿真弹道倾角。FIG. 4 shows the simulated trajectory inclination angles in Example 2 and Comparative Example 1 according to the present invention.
具体实施方式DETAILED DESCRIPTION
下面通过附图和实施例对本发明进一步详细说明。通过这些说明,本发明的特点和优点将变得更为清楚明确。The present invention will be further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present invention will become more clear and distinct.
在这里专用的词“示例性”意为“用作例子、实施例或说明性”。这里作为“示例性”所说明的任何实施例不必解释为优于或好于其它实施例。尽管在附图中示出了实施例的各种方面,但是除非特别指出,不必按比例绘制附图。The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise noted.
根据本发明提供的一种基于神经网络的自适应偏置比例导引方法,针对静止固定目标,利用神经网络获取偏置比例导引,从而实现飞行器精确的落脚约束。According to a neural network-based adaptive bias proportional guidance method provided by the present invention, a neural network is used to obtain bias proportional guidance for a stationary fixed target, thereby achieving precise footing constraint of an aircraft.
所述偏置比例导引的导引律可以表示为:The guidance law of the biased proportional guidance can be expressed as:
其中,θ为弹道倾角,q为弹目视线角,N为导引系数,b为偏置项。Among them, θ is the ballistic inclination angle, q is the sight angle between the projectile and the target, N is the guidance coefficient, and b is the bias term.
传统的偏置比例导引中,偏置项一般采用以下公式获得:In traditional bias proportional guidance, the bias term is generally obtained using the following formula:
其中,θf为终端交会角,θ0为初始弹道倾角,q0为初始弹目视线角,t0为飞行器初始发射时间,tf为飞行器终端交会时间。Among them, θf is the terminal rendezvous angle, θ0 is the initial trajectory inclination angle, q0 is the initial missile-target line of sight angle, t0 is the initial launch time of the aircraft, and tf is the terminal rendezvous time of the aircraft.
对于tf-t0有如下传统方法获得:There are the following traditional methods to obtain t f -t 0 :
t=tf-t0 t= tf - t0
其中,t为飞行器从发射开始到飞行至目标位置所用的飞行时间;由于tf-t0应用了小角度假设及对飞行剩余总时间的估算,导致其存在误差,导致最终获得的偏置比例导引的导引律精确度较低。Among them, t is the flight time taken by the aircraft from the start of launch to the flight to the target position; since tf - t0 applies the small angle assumption and the estimation of the remaining total flight time, it causes errors, resulting in the low accuracy of the guidance law of the biased proportional guidance finally obtained.
发明人发现,传统方法获得的tf-t0中存在的误差,可以通过加入角度偏差项Δη和时间偏差项Δt进行补偿:The inventors found that the error in t f -t 0 obtained by the traditional method can be compensated by adding the angle deviation term Δη and the time deviation term Δt:
其中,r为弹目距离,v为飞行器速度,均为正值,则|θf|是关于b的严格单调递增函数,而严格单调函数存在一一映射关系,即|θf|与b存在一一映射关系,可以表示为:Among them, r is the distance between the missile and the target, v is the speed of the aircraft, are all positive values, then |θ f | is a strictly monotone increasing function with respect to b, and a strictly monotone function has a one-to-one mapping relationship, that is, |θ f | and b have a one-to-one mapping relationship, which can be expressed as:
b=f-1(r,N,q0,θ0,θf)b=f -1 (r,N,q 0 ,θ 0 ,θ f )
进一步地,神经网络中的BP神经网络对这种映射关系能够较为精准的进行预测,因此,在本发明中,采用BP神经网络获取偏置比例导引。Furthermore, the BP neural network in the neural network can predict this mapping relationship more accurately. Therefore, in the present invention, the BP neural network is used to obtain the bias proportional guidance.
根据本发明,所述神经网络的输入为飞行器发射时的弹目距离r、初始弹道倾角θ0,初始弹目视线角q0和期望的终端交会角θf;According to the present invention, the input of the neural network is the missile-target distance r, the initial trajectory inclination angle θ 0 , the initial missile-target sight angle q 0 and the desired terminal intersection angle θ f when the aircraft is launched;
不同于传统方法中常数项以初始弹目视线角q0、初始弹道倾角θ0、期望的终端交会角θf和飞行时间t解算获得,在本发明中,不再使用误差较大的飞行时间t作为神经网络的输入,转而使用较容易进行测量的弹目距离r作为神经网络的输入,从而进一步提高了制导精度。Different from the conventional method in which the constant term is obtained by solving the initial missile-target sight angle q 0 , the initial trajectory inclination angle θ 0 , the expected terminal intersection angle θ f and the flight time t, in the present invention, the flight time t with a large error is no longer used as the input of the neural network, but the missile-target distance r which is easier to measure is used as the input of the neural network, thereby further improving the guidance accuracy.
进一步地,所述BP神经网络的输出为常数项b,在根据下式获得偏置比例导引的导引律:Furthermore, the output of the BP neural network is a constant term b, and the guidance law of the bias proportional guidance is obtained according to the following formula:
其中,θ为弹道倾角,q为弹目视线角,N为导引系数。Among them, θ is the ballistic inclination angle, q is the sight angle between the projectile and the target, and N is the guidance coefficient.
在一个优选的实施方式中,所述BP神经网络的隐藏层的个数为5个,发明人发现,5个隐藏层时,基于神经网络输出的常数项获得的导引律更为精确,且神经网络的计算速度较快,能够实现在线求解。In a preferred embodiment, the number of hidden layers of the BP neural network is 5. The inventors found that when there are 5 hidden layers, the guidance law obtained based on the constant term output by the neural network is more accurate, and the calculation speed of the neural network is faster, which can achieve online solution.
进一步优选地,所述BP神经网络的激活函数为sigmod函数:Further preferably, the activation function of the BP neural network is a sigmoid function:
进一步地,在使用BP神经网络前需要对其进行训练,在本发明中,采用弹道仿真获得训练样本。Furthermore, the BP neural network needs to be trained before use. In the present invention, trajectory simulation is used to obtain training samples.
具体地,所述弹道仿真为非线性打击模型仿真,所述非线性打击模型可以表示为:Specifically, the trajectory simulation is a nonlinear strike model simulation, and the nonlinear strike model can be expressed as:
η=θ-qη=θ-q
其中,η为飞行器速度与弹目视线的夹角,aM为飞行器的过载指令。Among them, η is the angle between the aircraft speed and the missile-target line of sight, and a M is the overload command of the aircraft.
进一步地,所述非线性打击模型中的积分采用龙格库塔法求解,优选地,求解步长为0.05。Furthermore, the integral in the nonlinear strike model is solved by the Runge-Kutta method, and preferably, the solution step is 0.05.
龙格库塔法(Runge-Kutta methods)是用于非线性常微分方程的解的重要的一类隐式或显式迭代法,在本发明中不做赘述。The Runge-Kutta method is an important implicit or explicit iterative method for solving nonlinear ordinary differential equations, which will not be described in detail in the present invention.
进一步地,在建立训练样本时,多次修改弹道仿真的设定参数,获得不同设定参数下的终端交会角。Furthermore, when establishing the training samples, the setting parameters of the trajectory simulation are modified multiple times to obtain the terminal intersection angle under different setting parameters.
进一步地,所述弹道仿真的设定参数包括初始弹目距离、导引系数、初始弹道倾角、初始弹目视线角。Furthermore, the setting parameters of the trajectory simulation include an initial projectile-target distance, a guidance coefficient, an initial trajectory inclination angle, and an initial projectile-target sight angle.
在一个优选的实施方式中,在设定参数中,初始弹目距离为5000米-10000米,导引系数2-4,初始弹道倾角10-20°。In a preferred embodiment, in the setting parameters, the initial projectile-target distance is 5000 meters to 10000 meters, the guidance coefficient is 2-4, and the initial ballistic inclination angle is 10-20 degrees.
在一个优选的实施方式中,初始弹目距离每隔200m取一组数据,导引系数分别为2、3、4,初始弹道倾角间隔0.2°取一组数据,作为弹道仿真的设定参数,从而获得较为全面的训练样本。In a preferred embodiment, a set of data is taken every 200 m of the initial projectile-target distance, the guidance coefficients are 2, 3, and 4 respectively, and a set of data is taken every 0.2° of the initial trajectory inclination angle as setting parameters for the trajectory simulation, thereby obtaining a more comprehensive training sample.
在一个优选的实施方式中,根据初始弹目距离调整训练样本中弹道仿真的设定参数,当初始弹目距离大于7500m时,每间隔100m取一组数据,导引系数分别为2、3、4,初始弹道倾角间隔0.2°取一组数据,作为弹道仿真的设定参数,以保证训练出的神经网络能够获得更好的精度;In a preferred embodiment, the setting parameters of the trajectory simulation in the training sample are adjusted according to the initial projectile-target distance. When the initial projectile-target distance is greater than 7500m, a set of data is taken at intervals of 100m, and the guidance coefficients are 2, 3, and 4 respectively, and a set of data is taken at intervals of 0.2° for the initial trajectory inclination angle as the setting parameters of the trajectory simulation, so as to ensure that the trained neural network can obtain better accuracy.
当初始弹目距离小于7500m时,每间隔200m取一组数据,导引系数分别为2、3、4,初始弹道倾角间隔0.2°取一组数据,作为弹道仿真的设定参数,由于初始弹目距离较近,在保证制导精度的前提下可以适量降低样本量,从而降低神经网络的运算量,减少弹载计算机的解算能力,即无需高配置的弹载计算机,降低成本。When the initial missile-target distance is less than 7500m, a set of data is taken at intervals of 200m, and the guidance coefficients are 2, 3, and 4 respectively. A set of data is taken at intervals of 0.2° for the initial trajectory inclination angle as the setting parameters for the trajectory simulation. Since the initial missile-target distance is relatively close, the sample size can be appropriately reduced while ensuring the guidance accuracy, thereby reducing the amount of computation of the neural network and the solving capability of the onboard computer, that is, there is no need for a high-configuration onboard computer, thus reducing costs.
根据本发明,在训练神经网络的过程中,从训练样本中选取60%-80%的样本作为训练集,例如70%的样本作为训练集,10%-20%的样本作为测试集,例如15%的样本作为测试集,10%-20%的样本作为验证集,例如15%的样本作为验证集。According to the present invention, in the process of training a neural network, 60%-80% of the samples are selected from the training samples as training sets, for example, 70% of the samples are selected as training sets, 10%-20% of the samples are selected as test sets, for example, 15% of the samples are selected as test sets, and 10%-20% of the samples are selected as validation sets, for example, 15% of the samples are selected as validation sets.
进一步优选地,在训练神经网络的过程中,采用Adam学习率对神经网络的参数进行更新,更新过程可以表示为:Further preferably, in the process of training the neural network, the parameters of the neural network are updated using the Adam learning rate, and the updating process can be expressed as:
其中,为更新前参数;为更新后参数;κ为学习率;ε为平滑项,防止被零除;m′t为一阶矩估计,v′t为二阶矩估计;mt为梯度一阶矩,vt为梯度二阶矩,β1、β2为常值指数衰减率。in, is the parameter before updating; is the updated parameter; κ is the learning rate; ε is the smoothing term to prevent division by zero; m′ t is the first-order moment estimate, v′ t is the second-order moment estimate; m t is the first-order moment of the gradient, v t is the second-order moment of the gradient, β 1 and β 2 are constant exponential decay rates.
根据本发明,所述飞行器发射时的初始弹道倾角优选为10-20°,发明人发现,初始弹道倾角对终端交会角精度(即实际终端交会角与期望终端交会角的误差)有较大影响,当初始弹道倾角过小时,终端交会角精度明显降低,而初始弹道倾角偏大时,不利于工程发射,亦影响终端交会角精度,更优选地,所述初始弹道倾角为15-20°,在此范围内,终端交会角的精度最好。According to the present invention, the initial ballistic inclination angle of the aircraft when launched is preferably 10-20°. The inventors have found that the initial ballistic inclination angle has a great influence on the accuracy of the terminal intersection angle (i.e., the error between the actual terminal intersection angle and the expected terminal intersection angle). When the initial ballistic inclination angle is too small, the accuracy of the terminal intersection angle is significantly reduced. When the initial ballistic inclination angle is too large, it is not conducive to engineering launch and also affects the accuracy of the terminal intersection angle. More preferably, the initial ballistic inclination angle is 15-20°. Within this range, the accuracy of the terminal intersection angle is best.
实施例Example
实施例1Example 1
基于神经网络获取自适应偏置比例导引,偏置比例导引的导引律为:Based on the neural network, the adaptive bias proportional guidance is obtained. The guidance law of the bias proportional guidance is:
采用BP神经网络获取偏置比例导引中的常数项b,其中神经网络的隐藏层的个数为5个,激活函数为sigmod函数。The BP neural network is used to obtain the constant term b in the bias proportional guidance, where the number of hidden layers of the neural network is 5 and the activation function is the sigmoid function.
训练样本通过弹道仿真获得,弹道仿真为非线性打击模型仿真,所述非线性打击模型为:The training samples are obtained through ballistic simulation, which is a nonlinear strike model simulation. The nonlinear strike model is:
η=θ-qη=θ-q
非线性打击模型中的积分采用龙格库塔法求解,求解步长为0.05,弹道仿真的设定参数包括初始弹目距离、导引系数、初始弹道倾角、初始弹目视线角,其中,初始弹目距离为5000米-10000米,导引系数2-4,初始弹道倾角10-20°,初始弹目距离每隔200m取一组数据,导引系数分别为2、3、4,初始弹道倾角间隔0.2°取一组数据。The integral in the nonlinear strike model is solved by the Runge-Kutta method with a solution step of 0.05. The setting parameters of the ballistic simulation include the initial projectile-target distance, guidance coefficient, initial ballistic inclination, and initial projectile-target line of sight angle. The initial projectile-target distance is 5000 meters to 10000 meters, the guidance coefficient is 2-4, the initial ballistic inclination is 10-20°, and a set of data is taken every 200 meters for the initial projectile-target distance. The guidance coefficients are 2, 3, and 4 respectively, and a set of data is taken every 0.2° for the initial ballistic inclination.
从训练样本中选取70%的样本作为训练集,15%的样本作为测试集,15%的样本作为验证集。70% of the samples are selected from the training samples as the training set, 15% of the samples are selected as the test set, and 15% of the samples are selected as the validation set.
在训练神经网络的过程中,采用Adam学习率对神经网络的参数进行更新,更新过程表示为:In the process of training the neural network, the Adam learning rate is used to update the parameters of the neural network. The update process is expressed as:
采用训练好的BP神经网络进行仿真,仿真参数中期望终端交会角θf分别设为-30°、-60°、-90°,其余仿真参数如表一所示:The trained BP neural network is used for simulation. The expected terminal intersection angle θf in the simulation parameters is set to -30°, -60°, and -90° respectively. The other simulation parameters are shown in Table 1:
表一Table 1
根据上述参数可知,神经网络的输入参数为:弹目距离r=10000,初始弹道倾角θ0=10°,初始弹目视线角q0=0°,期望的终端交会角θf分别为-30°、-60°、-90°。According to the above parameters, the input parameters of the neural network are: projectile-target distance r=10000, initial trajectory inclination angle θ 0 =10°, initial projectile-target sight angle q 0 =0°, and the expected terminal intersection angles θ f are -30°, -60°, and -90° respectively.
根据BP神经网络获得偏置比例导引导引律,该导引律的仿真弹道轨迹如图2所示,弹道倾角如图3所示,从图中可以看出,在三种情况下飞行器均可以到达目标位置,实际终端交会角分别为:-29.9908°、-60.0016°、-89.9976°,与期望终端交会角差距很小。The biased proportional guidance law is obtained according to the BP neural network. The simulated ballistic trajectory of the guidance law is shown in Figure 2, and the ballistic inclination is shown in Figure 3. It can be seen from the figure that the aircraft can reach the target position in all three cases, and the actual terminal intersection angles are -29.9908°, -60.0016°, and -89.9976°, respectively, which are very close to the expected terminal intersection angle.
实施例2Example 2
采用与实施例1相同的方法进行仿真,区别在于,仿真参数中,期望终端交会角θf分别设为-40°、-55°,其余仿真参数如表二所示:The simulation is performed in the same manner as in Example 1, except that, in the simulation parameters, the expected terminal intersection angle θf is set to -40° and -55° respectively, and the remaining simulation parameters are shown in Table 2:
表二Table 2
仿真获得的弹道倾角轨迹如图4所示,飞行器实际终端交会角分别为-40.21和-54.97°。The ballistic inclination trajectory obtained by simulation is shown in Figure 4. The actual terminal intersection angles of the aircraft are -40.21 and -54.97° respectively.
对比例1Comparative Example 1
进行与实施例2相同的仿真实验,区别在于,采用传统的公式求解获得偏置比例导引的导引律,该导引律的仿真弹道倾角如图4所示,飞行器实际终端交会角分别为-41.95°-61.14°。The same simulation experiment as in Example 2 is performed, except that the conventional formula is used to solve the guidance law of the offset proportional guidance. The simulated ballistic inclination angle of the guidance law is shown in FIG4 , and the actual terminal intersection angles of the aircraft are -41.95°-61.14°, respectively.
对比实施例2与对比例1的仿真弹道倾角,可以看出,实施例2获得的偏置比例导引的导引律精度更高,实际交会角与期望交会角误差降低了9倍以上,尤其在期望终端交会角大时,实际交会角与期望交会角误差降低了近38倍,实际交会角与期望交会角误差低至0.05%,制导精度提高效果明显。By comparing the simulated ballistic inclination angles of Example 2 and Comparative Example 1, it can be seen that the guidance law accuracy of the offset proportional guidance obtained in Example 2 is higher, and the error between the actual intersection angle and the expected intersection angle is reduced by more than 9 times, especially when the expected terminal intersection angle is large, the error between the actual intersection angle and the expected intersection angle is reduced by nearly 38 times, and the error between the actual intersection angle and the expected intersection angle is as low as 0.05%, and the guidance accuracy is significantly improved.
在本发明的描述中,需要说明的是,术语“上”、“下”、“内”、“外”、“前”、“后”等指示的方位或位置关系为基于本发明工作状态下的方位或位置关系,仅是为了便于描述本发明和简化描述,而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作,因此不能理解为对本发明的限制。此外,术语“第一”、“第二”、“第三”、“第四”仅用于描述目的,而不能理解为指示或暗示相对重要性。In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front", "back" and the like indicate positions or positional relationships based on the working state of the present invention, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", and "fourth" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.
在本发明的描述中,需要说明的是,除非另有明确的规定和限定,术语“安装”“相连”“连接”应作广义理解,例如,可以是固定连接,也可以是可拆卸连接,或一体的连接普通;可以是机械连接,也可以是电连接;可以是直接连接,也可以通过中间媒介间接连接,可以是两个元件内部的连通。对于本领域的普通技术人员而言,可以具体情况理解上述术语在本发明中的具体含义。In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.
以上结合了优选的实施方式对本发明进行了说明,不过这些实施方式仅是范例性的,仅起到说明性的作用。在此基础上,可以对本发明进行多种替换和改进,这些均落入本发明的保护范围内。The present invention has been described above in conjunction with preferred embodiments, but these embodiments are only exemplary and serve only as an illustration. On this basis, the present invention may be subjected to a variety of substitutions and improvements, all of which fall within the scope of protection of the present invention.
Claims (10)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111137643.7A CN115879357A (en) | 2021-09-27 | 2021-09-27 | Self-adaptive bias proportion guidance method based on neural network |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111137643.7A CN115879357A (en) | 2021-09-27 | 2021-09-27 | Self-adaptive bias proportion guidance method based on neural network |
Publications (1)
Publication Number | Publication Date |
---|---|
CN115879357A true CN115879357A (en) | 2023-03-31 |
Family
ID=85763051
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202111137643.7A Pending CN115879357A (en) | 2021-09-27 | 2021-09-27 | Self-adaptive bias proportion guidance method based on neural network |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN115879357A (en) |
-
2021
- 2021-09-27 CN CN202111137643.7A patent/CN115879357A/en active Pending
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN108645427A (en) | The used system-level temperature-compensation method of group of laser based on spline interpolation iterated revision | |
CN107491080A (en) | A kind of Spacecraft Attitude Control tracked based on nonlinear feedback and differential | |
CN112711816B (en) | Flight projectile trajectory correction method based on meteorological grid | |
CN111591470A (en) | A Closed-loop Guidance Method for Accurate Soft Landing of Aircraft Adapting to Adjustable Thrust Mode | |
CN116071051B (en) | Method, system, electronic equipment and medium for determining optimal replacement time of inertial navigation system | |
CN111158386B (en) | Unmanned aerial vehicle attitude control system for interfering autonomous immunity and implementation method | |
CN107423556A (en) | A kind of computational methods of the Long Range Rocket Gun launch data based on RBF agent model | |
CN112069605A (en) | A Design Method of Proportional Guidance Law with Attack Time Constraint | |
CN107607105A (en) | Optical fibre gyro nonlinear temperature error compensating method based on fractional order differential | |
CN108508463B (en) | A Fourier-Hermite Orthogonal Polynomial Extended Ellipsoid Set Membership Filtering Method | |
CN111856933B (en) | Design method of self-adaptive controller of aircraft engine considering uncertainty | |
CN113758380A (en) | Guidance control integration method considering steering engine dynamic performance and attack angle constraint | |
CN114611416B (en) | LS-SVM modeling method for nonlinear unsteady aerodynamic characteristics of missile | |
CN114063458B (en) | A Preset Performance Control Method for Non-triangular Structure Systems Independent of Initial Conditions | |
CN115879357A (en) | Self-adaptive bias proportion guidance method based on neural network | |
CN110826288A (en) | Reentry trajectory optimization method based on immune clone selection | |
CN119556709A (en) | A fast fitting method for nonlinear state of aircraft | |
CN109582915B (en) | Improved nonlinear observability self-adaptive filtering method applied to pure azimuth tracking | |
CN104834330B (en) | Liquid floated pendulum type accelerometer temperature control model parameter identification method | |
CN116184830B (en) | A method for controlling the opening of a cage-type electric throttle valve | |
CN110567490A (en) | A SINS Initial Alignment Method under Large Misalignment Angle | |
CN116520876A (en) | Guidance law design method for optimizing observability | |
CN110082749A (en) | Shell outer trajectory state of flight estimation method | |
CN110995203B (en) | Nonlinear observability degree analysis method based on condition number fusion | |
CN111043914B (en) | Method and system for acquiring bullet sight line angular rate applied to strapdown seeker |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination |